Yesheng Ma is a software engineer and MCDS student at Carnegie Mellon University with 10 years of experience building high-performance systems and distributed services. Currently at Databricks working on Unity Catalog, he has deep expertise in Spark internals from prior roles including an interactive Spark service at Facebook and contributions to Apache Spark's SQL parser and optimizer. His background blends research (CMU Database Group, ADAPT Lab) and industry internships (Baidu, NUS), focusing on OS-level system programming, HTAP databases, and reliable distributed algorithms. A fan of functional programming, he has a proven eye for robustness and performance—having improved SQL error reporting, hyphenated identifier handling, and optimizer latency in a major open-source analytics engine. Based in Mountain View, he pairs rigorous academic training and strong grades with hands-on production experience at scale. An under-the-radar strength is his knack for turning research insights into tangible engine-level optimizations that speed query planning and correctness.
10 years of coding experience
3 years of employment as a software developer
Master of Computational Data Science, Systems, 4.02/4.33, Master of Computational Data Science, Systems, 4.02/4.33 at Carnegie Mellon University
Bachelor's degree, Computer Science, 89.55/100, Bachelor's degree, Computer Science, 89.55/100 at Shanghai Jiao Tong University
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Contributions:4 reviews, 1 commit, 35 PRs in 1 day
Contributions summary:Yesheng primarily contributed to improving the SQL parsing capabilities of Apache Spark, focusing on making the parser more robust and user-friendly. They made significant changes to the parser to handle hyphenated identifiers, improve error messages for incorrect SQL syntax, and optimize the processing of floating-point numbers. Furthermore, they addressed performance bottlenecks within the optimizer by converting methods to lazy vals and improving predicate pushdown, resulting in faster query planning times.
Contributions:21 commits, 18 pushes, 1 branch in 2 months
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